Preprints and Publications

  1. Fang, F., Bai, S., & Wang, T. (2025). Structural causal models for extremes: An approach based on exponent measures. arXiv. [Online]
  2. Fang, F., Airoldi, E. M., & Forastiere, L. (2025). Inward and outward spillover effects of one unit’s treatment on network neighbors under partial interference. arXiv. [Online]
  3. Wu, Y., Fang, F. Association of antihypertensive medication and steatotic liver disease with liver fibrosis and mortality among US adults. medRxiv, (2025). Link
  4. Jiang, C., Fang, F., Talbot, D., & Schnitzer, M. E. Estimating direct and spillover vaccine effectiveness with partial interference under test-negative design sampling. medRxiv, (2025). Link
  5. Arlotto, A., Belloni, A., Fang, F., & Pekec, S. Ballot design and electoral outcomes: The role of candidate order and party affiliation. Working paper, (2025). Link
  6. Wu, Y., Fang, F. Validating the new nomenclature of steatotic liver disease in patients with excessive alcohol intake. The Lancet Gastroenterology & Hepatology, 9(5), 409 (2024). Link
  7. Wu, Y., Fang, F., Fan, X., & Nie, H. Associations of cannabis use, metabolic dysfunction-associated steatotic liver disease, and liver fibrosis in US adults. Cannabis and Cannabinoid Research, (2024). Link
  8. Belloni, A., Fang, F., & Volfovsky, A. Neighborhood adaptive estimators for causal inference under network interference. arXiv preprint arXiv:2212.03683, (2022). Link
  9. Fang, F., Sussman, D. L., & Lyzinski, V. Tractable graph matching via soft seeding. arXiv preprint arXiv:1807.09299, (2018). Link
  10. Fang, F., Sun, Y., & Spiliopoulos, K. On the effect of heterogeneity on flocking behavior and systemic risk. Statistics & Risk Modeling, 34(3–4), 141–155 (2017). Link